Reactome pathway analysis: a high-performance in-memory approach
By A. Fabregat, K. Sidiropoulos, Guilherme Viteri, Oscar Forner-Martinez, P. Marín-García, Vicente Arnau, P. D’Eustachio, Lincoln D. Stein, H. Hermjakob. BMC Bioinformatics, 2017
1,120 citations recorded by the source corpus.
Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub (https://github.com/reactome/).
Subfields: Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/6fe805d1646f0dc4ad661e3e6760b367d6dccc61
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In Molecular Biology
- Ryuji Hamamoto
- S. Hayami
- Syuzo Kaneko
- Masaaki Komatsu
- Ken Asada
- Amina Bolatkan
- H. Yamaue
- Lincoln D. Stein
Citations in the corpus
- Deregulation of the Histone Lysine-Specific Demethylase 1 Is Involved in Human Hepatocellular Carcinoma (2019, background): “05 as the threshold of this analysis, a limited number of signaling pathways were identified [40]; those were mostly related to the RA pathway, the FGFR and/or Klotho-mediated ligand binding pathway, and the MST-1-mediated signaling pathway (Figure 4D and Supplementary Figures S2–S5).”
- The Reactome Pathway Knowledgebase (2017, methodology): “The Reactome implementation uses a hypergeometric distribution test to generate a probability score, which is corrected for false discovery rate using the Benjamani-Hochberg method (9,10).”